{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## oob和更多Bagging相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### oob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "BaggingClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n                                                        class_weight=None,\n                                                        criterion='gini',\n                                                        max_depth=None,\n                                                        max_features=None,\n                                                        max_leaf_nodes=None,\n                                                        min_impurity_decrease=0.0,\n                                                        min_impurity_split=None,\n                                                        min_samples_leaf=1,\n                                                        min_samples_split=2,\n                                                        min_weight_fraction_leaf=0.0,\n                                                        presort='deprecated',\n                                                        random_state=None,\n                                                        splitter='best'),\n                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n                  max_samples=100, n_estimators=500, n_jobs=None,\n                  oob_score=True, random_state=None, verbose=0,\n                  warm_start=False)"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "# oob_score必须指定为True\n",
    "# 否则不记录到底取了那些样本\n",
    "bagging_clf = BaggingClassifier(DecisionTreeClassifier(),\n",
    "                               n_estimators=500, max_samples=100,\n",
    "                               bootstrap=True, oob_score=True)\n",
    "bagging_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.918"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "bagging_clf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Wall time: 733 ms\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "BaggingClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n                                                        class_weight=None,\n                                                        criterion='gini',\n                                                        max_depth=None,\n                                                        max_features=None,\n                                                        max_leaf_nodes=None,\n                                                        min_impurity_decrease=0.0,\n                                                        min_impurity_split=None,\n                                                        min_samples_leaf=1,\n                                                        min_samples_split=2,\n                                                        min_weight_fraction_leaf=0.0,\n                                                        presort='deprecated',\n                                                        random_state=None,\n                                                        splitter='best'),\n                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n                  max_samples=100, n_estimators=500, n_jobs=None,\n                  oob_score=True, random_state=None, verbose=0,\n                  warm_start=False)"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "%%time\n",
    "bagging_clf = BaggingClassifier(DecisionTreeClassifier(),\n",
    "                               n_estimators=500, max_samples=100,\n",
    "                               bootstrap=True, oob_score=True)\n",
    "bagging_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Wall time: 1.55 s\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "BaggingClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n                                                        class_weight=None,\n                                                        criterion='gini',\n                                                        max_depth=None,\n                                                        max_features=None,\n                                                        max_leaf_nodes=None,\n                                                        min_impurity_decrease=0.0,\n                                                        min_impurity_split=None,\n                                                        min_samples_leaf=1,\n                                                        min_samples_split=2,\n                                                        min_weight_fraction_leaf=0.0,\n                                                        presort='deprecated',\n                                                        random_state=None,\n                                                        splitter='best'),\n                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n                  max_samples=100, n_estimators=500, n_jobs=-1, oob_score=True,\n                  random_state=None, verbose=0, warm_start=False)"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "%%time\n",
    "bagging_clf = BaggingClassifier(DecisionTreeClassifier(),\n",
    "                               n_estimators=500, max_samples=100,\n",
    "                               bootstrap=True, oob_score=True,\n",
    "                               n_jobs=-1)\n",
    "bagging_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bootstrap_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.836"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "# max_features: 最大特征数\n",
    "# bootstrap_features：特征随机采样是放回采样\n",
    "random_subspaces_clf = BaggingClassifier(DecisionTreeClassifier(),\n",
    "                               n_estimators=500, max_samples=500,\n",
    "                               bootstrap=True, oob_score=True,\n",
    "                               max_features=1, bootstrap_features=True)\n",
    "random_subspaces_clf.fit(X, y)\n",
    "random_subspaces_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.858"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "random_patches_clf = BaggingClassifier(DecisionTreeClassifier(),\n",
    "                               n_estimators=500, max_samples=100,\n",
    "                               bootstrap=True, oob_score=True,\n",
    "                               max_features=1, bootstrap_features=True)\n",
    "random_patches_clf.fit(X, y)\n",
    "random_patches_clf.oob_score_"
   ]
  }
 ],
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